01: AI - The dawn of the data age

26 February 2019 Nordea Markets and Nordea Corporate & Investment Banking Companies need a data strategy to exploit AI Nordea's Global Co-Head of Corporate & Investment Banking Mathias Leijon describes how artiﬁcial intelligence could lower inflation and boost productivity on the macro level. He argues companies will need to change leadership, decision-making dynamics and corporate culture to overcome hurdles posed by legacy systems and structures. In so doing, they can articulate a data strategy and embrace and exploit AI and machine learning. JT: Generally, how do you think the rapid development of artiﬁcial intelligence in recent years will affect the economy and corporates? ML: In the ﬁeld of AI, there are so many areas that are relevant, and it is easy to be directional and look ahead and extrapolate – and the more futuristic that AI becomes, the more attention it captures. The reality today is that we have come further than we thought, but at the same time we have not moved the needle in comparison with the vast possibilities. Take Waymo owned by Alphabet (Google) as an example, which develops autonomous cars. In 2018, during the operation of its self-driving vehicles, there was only one manual driver intervention per 11,018 miles driven in California. This is truly amazing. Another area that will be, and most likely already is, impacted is inflation. As many goods and services are new, have zero marginal costs and are offered in highly competitive environments through digital platforms, the result is lower prices. Potentially (or some would argue, likely), traditional statistics fail to capture the increases in real customer value at lower costs. Taken together with many of these new players being loss-making, this understates the actual underlying and likely future inflation. A third exciting area for discussion could be the productivity explosion that we will probably see over the coming decades. JT: From what you are seeing, are companies ready to adopt and exploit AI in their businesses? Waymo's self-driving car in California ML: One area that is not discussed enough is how we, as leaders, and our organisations need to develop and evolve in response to this. Machine learning comes with great promise and will improve services at scale. But in order to do so, corporates need a completely different level of automation and streamlining of operations. On top of this comes what will be the future core of business – the data strategy, ie how to ensure classiﬁcation of data in a stringent and scalable way to allow for deep analytics, extraction of information and the ability to add and improve the services provided. This is a major task for any company that was not founded at the start of "the App era". Organisations that existed well before the "App era" have, over time, reorganised, acquired or sold companies, and installed a myriad of software systems and customer classiﬁcations. To address this lack of streamlining, a different decision-making logic will be needed throughout these organisations, where currently the front line (sales) often drives the decisions, whereas the back office (back-end IT) and mid-office have merely been the support systems. A new logic requires much more decision-making power in the mid-office, which is where data is an important component in most organisations and which will be even more instrumental for value creation. In such a process, the leadership needs to evolve. How have we, as leaders, adapted to be able to motivate and lead data scientists and millennials? At the same time, have we adapted to constantly ensure that the cost for the individual, society and the corporation is minimised, even in the face of likely higher staff turnover owing to accelerating change? Against this backdrop, new competence will become increasingly important. This includes the level of knowledge about machine learning and its various subﬁelds, an understanding of how to approach a speciﬁc set of customer processes, the ability to participate in the dialogue around which algorithm to choose and how it can be tweaked, and, of course, knowing how value is created for the customer. For larger organisations, the distance between the decision makers and the actual execution needs to shrink. Looking ahead, there probably needs to be, to various degrees, an increased rotation between the two in order to foster an up-to-date understanding. The changes in consumer trends and demand are accelerating. How then can leaders keep up and be attuned to all these external changes, at the same 2